---
title: "Basic Graphical Displays"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: minty
      navbar-bg: "green"
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library (DT)
library(plotly)
Friday <- read_csv("./Black_Friday.csv")
```

Brief Overview 1
===

Column {data-width=450}
---

In this session, we use the Black Friday Data available in [Kaggle](https://www.kaggle.com/datasets/pranavuikey/black-friday-sales-eda) to study hpw to make the following graphical dislays.

Column {.tabset data-width=550}
---

### Graphical Displays
- Categorical Data
  - Bar Chart
  - Pie Chart

- Quantitative Data
  - Histogram
  - Boxplot
  - Scatterplot
  - Line
  
### Common Arguments
Here is a list of common arguments

  - col: a vector of colors
  - main: title for the plot
  - xlim or ylim: limits for the x or y axis
  - xlab or ylab: a label for the x axis
  - font: font used for text, 1=plain; 2=bold; 3=italic, 4=bold italic
  - font.axis: font used for axis
  - cex.axis: font size for x and y axes
  - font.lab: font for x and y labels
  - cex.lab: font size for x and y labels

Brief Overview 2 {data-orientation=rows}
=== 

Row {data-height=100}
---

In this session, we use the Black Friday Data available in [Kaggle](https://www.kaggle.com/datasets/pranavuikey/black-friday-sales-eda) to study hpw to make the following graphical dislays.

Row {data-height=900}
---

### Graphical Displays
- Categorical Data
  - Bar Chart
  - Pie Chart

- Quantitative Data
  - Histogram
  - Boxplot
  - Scatterplot
  - Line
  
### Common Arguments
Here is a list of common arguments

  - col: a vector of colors
  - main: title for the plot
  - xlim or ylim: limits for the x or y axis
  - xlab or ylab: a label for the x axis
  - font: font used for text, 1=plain; 2=bold; 3=italic, 4=bold italic
  - font.axis: font used for axis
  - cex.axis: font size for x and y axes
  - font.lab: font for x and y labels
  - cex.lab: font size for x and y labels  

Data
===

Column {data-width=550}
---

### <b><font size = 4><span Style = "color:blue">First 500 Observations</span></font></b>

```{r show_table}
datatable(Friday[1:500,], rownames = FALSE, colnames = c("USer ID", "Product ID", "Gender", "Age", "Occupation", "City Category", "Stay In Current City Years", "Marital Status", "Product Category 1", "Product Category", "Product Category 3", "Purchase"), options = list ( pageLength = 20))
```

Column {data-width=450}
---

### <font size = 4><span Style = "color:red">Description</span></font>

In order to understand the customer purchases behavior against various products of different categories, the retail company "ABC Private Limited", in United Kingdom, shared purchase summary of various customers for selected high volume products from last month. The data contain the following variables.
 
- User_ID: User ID
- Product_ID: Product ID
- Gender: Sex of User
- Age: Age in bins
- Occupation: Occupation (Masked)
- City_Category: Category of the City (A,B,C)
- Stay_In_Current_City_Years: Number of years stay in current city
- Marital_Status: Marital Status
- Product_Category_1: Product Category (Masked)
- Product_Category_2: Product may belongs to other category also (Masked)
- Product_Category_3: Product may belongs to other category also (Masked)

```{r}
glimpse (Friday)
```

Bar Chart {data-orientation=rows}
===

Row {data-height=350}
---

###
Bar chart is a graphical display good for the general audience. Here, we study the distribution of Age Group of the company's customers who purchased their products on Black Friday.
**Usage:** barplot(height, ...)

A bar chart can be horizontal or vertical. Using the argument <span Style= "color:orange">col</span>, we can assign a color for bars. The argument <span Style="color:orange">main</span> could be used to change the title of the figure. We can use RGB color code to assign colors.

**Note:** The margin of a figure could be set using the <span Style="color:blue">par()</span> function. The order of the setting is <span Style ="color:orange">c(bottom, left, top, right)</span>.

### Analysis

Row {data-height=650}
---

### **Vertical Bar Chart**
```{r bar1}
par(mpg=c(4,1,0)) #change the margin line for the axis title, axis labels and axis line
par(mar=c(5,7,4,2)) # set margin of the figure
Friday %>%
  ggplot(aes(x = Age)) +
  geom_bar(fill = "#69b3a2") +
  coord_flip () +
  labs (title = "Distribution of Purchases by Customer's Age",
        x = "Age Groups",
        y = "Number of Purchases") -> bar1
ggplotly (bar1)
```

### **Horizontal Bar Chart**
```{r bar2}
par(mpg=c(4,1,0)) #change the margin line for the axis title, axis labels and axis line
par(mar=c(5,7,4,2)) # set margin of the figure
Friday%>%
  ggplot(aes(x = Age)) +
  geom_bar(fill="#69b3a2") +
  coord_flip() +
  labs(title = "Distribution of Purchases by Customer's Age",
       x = "Age Groups",
       y = "Number of Purchases") -> bar1
ggplotly(bar1)
```
Pie Chart
===

Column {data-width=500}
---

Similarly, we can use pie chart to study the distribution of the city category.

**Usage:** pie(height, ...)

**Tip:** Use color palette to choose colors (Google search: color scheme generator).

### Analysis

Column {data-width=500}
---

###Distribution of City Category
```{r pie}
H <- table (Friday$City_Category)
percent <- round(100*H/sum(H), 1) # calculate percentages
pie_labels <- paste(percent, "%", sep="") # include %
pie (H, main = "Distribution of City Category", labels = pie_labels, col = c ("#54d2d2", "#ffcb00", "#f8aa4b"))
legend("topright", c("A", "B", "C"), cex = 0.8, fill = c("#54d2d2", "#ffcb00", "#f8aa4b"))
```

Histogram
===

Column {data-width=500}
---

###
Histogram is sued when we want to study the distribution of a quantitative variable. Here we study the distribution of customer purchase amount

**Usage:** hist(x, ...)

```{r histogram}
Friday %>% ggplot (aes (x=Purchase)) + geom_histogram (fill="blue")+
  labs(title= "Distribution of Customer Purchase Amount",
       x= "Purchase Amount (British Pounds)")
```

Column {data-width=500}
---

### Analysis

Boxplot
===

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---

### Boxplot 1

### B1

```{r boxplot1}
boxplot(Friday$Purchase, xlab="Purchase Amount", ylab = "British Pounds")
```

### B2

```{r boxplot2}
boxplot (Purchase ~ Gender + Marital_Status, data = Friday, main = "Distribution of Purchase by Sex and Marital_Status",
         xlab="Sex and Marital Status", ylab = "Purchase", cex.lab=0.75,
         cex.axis=0.5,
         names = c("Female & Single", "Male & Single", "Female & Married", "Male & Married"))
```

### Boxplot 2

In general, a boxplot is used when we want to compare the distributions of several quantitative variables. In the following we study the distribution of customer purchase amount among different age groups.

Column {data-width=450}
---

### Analysis of Boxplot 1

### Analysis of Boxplot 2

Scatterplot
===

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---

###
When we want to study the relationship of two quantitative variables, a scatterplot can be used. Since this data set doesn't have another quantitative variable, we will use the built-in data <span class="orange">mtcars</span> in R. Then we study the relationship of miles per gallon against the weight of vehicles.

```{r scatterplot}
plot(mpg ~ wt, data=mtcars,
     xlab = "Weight (1000 lbs)", ylab = "Miles per Gallon",
     pch = 19, col = "blue")
```

Column {data-width = 500}
---

### Analysis 

Line Plot
===

Column {.tabset data-width=350}
---

### Data
Since the Black Friday Data are not time series data, it is not appropriate to use a line plot. In the following code chunk, we create a data frame using the forcasted highest temperatures from July 13 to July 22 in 2022 ([The Weather Channel] (https://weather.com/)).

```{r data}
Date <- 13:22
Dayton_OH <- c(84, 86, 91, 89, 89, 91, 92, 91, 91, 91)
Houston_TX <- c(100, 97, 96, 94, 94, 94, 93, 93, 92, 91)
Denver_CO <- c(95, 85, 89, 96, 97, 96, 92, 91, 95, 96)
Fargo_ND <- c(86, 80, 84, 87, 90, 87, 83, 84, 87, 89)
df <- data.frame(Date, Dayton_OH, Houston_TX, Denver_CO, Fargo_ND)
datatable(df, rownames = FALSE, colnames = c ("Date", "Dayton, OH", "Houston, TX", "Denver, CO", "Fargo, ND"))
```

### Analysis

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---

# Line Chart

```{r line1}
plot(Date, Dayton_OH, type="o", col="blue", xlab = "Date in July", ylab="Highest Temperature", ylim=c(80, 100))
lines(Date, Houston_TX, type="o", col="red")
lines(Date, Denver_CO, type="o", col="purple")
lines(Date, Fargo_ND, type="o", col="darkgreen")
# Add a legend
legend("topright", # Position of the legend
       legend = c("Dayton, OH", "Houston, TX", "Denver, CO", "Fargo, ND"), # Labels
       col = c("blue", "red", "purple", "darkgreen"), # Colors
       lty = 1, # Line types
       pch = 1) # Point types
        
```